CN113587977A - Old dangerous house collapse dynamic monitoring method based on multi-element sensing data - Google Patents
Old dangerous house collapse dynamic monitoring method based on multi-element sensing data Download PDFInfo
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Abstract
The invention discloses a dynamic monitoring method for old dangerous house collapse based on multivariate sensing data, which comprises the following steps: (1) carrying out limit load analysis, determining weak point positions, and setting a theoretical early warning threshold; (2) capturing data of relevant components of the critical house by adopting a sensor; (3) collecting data captured by a sensor, and sending the data to a server; (4) reading and processing data stored in the server, analyzing mechanical characteristics of the data, and performing threshold value comparison analysis; (5) and (4) early warning the possible safety accidents and arranging manual inspection. In the invention, a sensor is adopted to capture data such as inclination angle, displacement, strain and the like; collecting and sending sensor data to a server; then analyzing the data to judge the safety performance of the old dangerous house; early warning is carried out on possible safety accidents, and manual inspection is arranged; finally, the purpose of monitoring old houses in real time is achieved, and collapse accidents are prevented.
Description
Technical Field
The invention particularly relates to a dynamic monitoring method for old dangerous house collapse based on multi-element sensing data.
Background
Under the support of the internet technology and the cloud platform technology, the sensors are used for carrying out comprehensive safety monitoring and early warning on the existing building, and the mainstream development trend of urban house safety management at the present stage is to effectively avoid the risk caused by house collapse. The method creates more possibilities for safety monitoring of the remote house by means of the convenient and efficient cloud platform.
Meanwhile, the traditional detection mode needs to consume very huge costs of manpower, material resources and the like, and personnel in charge of the detection of the critical rooms must have professional knowledge and rich experience. The manual detection mainly depends on the result of subjective judgment, and human factors in the period may influence the effect of the crisis management and control or cause problems such as untimely early warning and the like.
Disclosure of Invention
Aiming at the situation, in order to overcome the defects of the prior art, the invention provides a dynamic monitoring method for the collapse of the old dangerous house based on multi-element sensing data.
In order to achieve the purpose, the invention provides the following technical scheme:
a dynamic monitoring method for old dangerous house collapse based on multivariate sensing data comprises the following steps:
(1) carrying out limit load analysis, determining weak point positions, and setting a theoretical early warning threshold;
(2) capturing data of relevant components of the critical house by adopting a sensor;
(3) collecting data captured by a sensor, and sending the data to a server;
(4) reading and processing the data stored in the server, analyzing the mechanical characteristics of the data, judging the safety level of the old dangerous house, and making early warning judgment;
(5) and (4) early warning the possible safety accidents and arranging manual inspection.
Further, in the step (2), the adopted sensor is any one or more of a stay rope type displacement sensor, a pull rod type displacement sensor, a vibrating wire type strain gauge, a static level gauge and an inclinometer.
Further, in the step (3), data captured by the sensor is collected through a Lora network; and packaging the data, converting the data into a 4G network and sending the data to a server.
Further, the step (5) is specifically as follows: the method has the advantages that the possible safety accidents are early warned, manual patrol is arranged, and old and dangerous rooms which are possible to happen are timely notified to relevant units to carry out crowd evacuation.
Further, in the step (2), a hydrostatic level is adopted to monitor the whole house and the settlement amount.
Further, a pull rod type displacement meter is adopted for crack monitoring.
Further, inclinometers are monitored for wall column inclination.
Further, in the step (3), the communication is carried out with the sensing node through the LORA network, and data is uploaded through the 4G module; the transmission data adopts a standard Json format.
Further, the strain gauge is adopted for monitoring the internal force of the bearing wall, and primary early warning is performed: the strain count value is more than or equal to 0.85 and less than or equal to 0.9 times of the design value or the calculated value of the bearing capacity; secondary early warning: the value of the strain gauge is more than or equal to 0.9 and less than 1.0 time of the design value or the calculated value of the bearing capacity; and (3) third-level early warning: the value of the strain gauge is more than or equal to 1.0 time of the designed or calculated value of the bearing capacity; gamma ray0·Sd≤RdThe design expression is calculated for the bearing capacity limit state; gamma ray0-coefficient of structural importance, Rd-design value of resistance of structure; sd-effect design values of load combinations.
Further, the single wall or column produces a limited local inclined deformation of more than 7% relative to the whole of the house, representing a structural risk.
The invention has the beneficial effects that:
(1) in the invention, a sensor is adopted to capture data such as inclination angle, displacement, strain and the like; collecting and sending sensor data to a server; then analyzing the data to judge the safety performance of the old dangerous house; early warning is carried out on possible safety accidents, and manual inspection is arranged; finally, the purpose of monitoring old houses in real time is achieved, and collapse accidents are prevented.
(2) According to the invention, objective safety evaluation and efficient management are carried out according to field data acquired by the sensor, the actual requirements of users can be met, and the management process of old dangerous houses is more intelligent and efficient.
Drawings
Fig. 1 is a general flowchart of a dynamic detection method.
Fig. 2 is a flow chart of the warning value setting module.
FIG. 3 is a flow diagram of a manual patrol module.
Fig. 4 is a schematic diagram of a network system architecture.
Fig. 5 is a flow chart of the operation of the sensing node.
Fig. 6 is a flow chart of gateway operation.
Fig. 7 is a computational model of a masonry structure, where the values represent the dimensions of the building in mm.
FIG. 8 is a calculated graph of compressive bearing capacity of a wall of a masonry structure, where the value is the bearing capacity of the wall in MPa.
FIG. 9 is a calculated graph of compressive bearing capacity of two walls of a masonry structure, where the values are the bearing capacity of the walls in MPa.
Fig. 10 is a stress cloud diagram of a double-opening weak wall of a certain masonry structure.
Fig. 11 is a schematic diagram of the dual-opening weak wall of a certain masonry structure collapsing at different time stages, wherein the collapse failure time of the masonry structure in (1) is earlier than that of the masonry structure in (2).
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
Example 1
The utility model provides an old and old dangerous house dynamic monitoring system that collapses based on many first sensing data, as shown in figure 1, includes that early warning threshold value sets up module, sensing module, data acquisition sending module, server, monitoring module, artifical module of patrolling, and sensing module is connected with data acquisition sending module, and data acquisition sending module is connected with the server, and the server is connected with monitoring module.
In some preferred ways, as shown in fig. 2, the early warning threshold setting module: carrying out limit load analysis, determining weak point positions and determining a theoretical early warning threshold; the early warning threshold value can be the collapse limit load, the integral inclination rate of the house, the settlement amount, the strain gauge threshold value and the like. Further, a first-level, a second-level and a third-level early warning threshold value can be set, and early warning information is issued when the feedback data exceeds the early warning threshold value.
The sensing module: capturing data of relevant components of the critical building by using a stay rope type displacement sensor, a pull rod type displacement sensor, a vibrating string type strain gauge, a static level gauge and an inclinometer; generally, the sensors are installed at corresponding positions after being analyzed according to site survey conditions or house design data, that is, after the early warning threshold setting module completes setting, the sensors are installed at corresponding positions.
The data acquisition and transmission module: collecting data captured by a sensing module through a Lora network; packaging the data, converting the data into a 4G network and sending the data to a server;
a server: receiving and storing data;
a monitoring module: and reading and processing the data stored in the server, analyzing the mechanical characteristics of the data, judging the safety level of the old dangerous house, and making early warning judgment.
The manual patrol module: the method has the advantages that the possible safety accidents are early warned, manual patrol is arranged, and old and dangerous rooms which are possible to happen are timely notified to relevant units to carry out crowd evacuation.
Further, the manual patrol module: when the early warning value is exceeded, corresponding emergency orders are dispatched according to different early warning levels, inspectors are informed to check the reasons on site in time, and meanwhile, records such as text images and the like are made. If the data is abnormal due to equipment failure, equipment replacement is reported in time. Classifying and applying strategies, and adjusting an early warning mechanism.
In some preferred modes, the early warning threshold setting module acquires original design data of a house; if the original design data does not exist, site reconnaissance is needed, a building plan is drawn, a structure construction drawing is drawn, material strength detection is carried out, and the structural form of the building is distinguished (for example, a house belongs to a masonry structure or a frame structure or a brick-wood structure); modeling, analyzing the limit load, determining weak components, and setting a theoretical early warning value. Determining the layout point positions and the number of the sensors, and selecting a proper network transmission mechanism according to the building position; drawing a plan view and an elevation view of the monitoring point; designing an early warning threshold value and determining the monitoring frequency.
In some preferred approaches, the sensor layout: determining the number of sensors in each category according to the sensor layout points; calibrating, debugging and testing the sensor before installation; completing field installation according to the sensor arrangement point positions, timely reporting sensors which cannot be installed due to field reasons, and rearranging the arrangement point positions; and after the sensors are arranged, performing secondary debugging on the sensors, and checking the smooth condition of background data.
In some preferred modes, in the data acquisition and transmission module, signal transmission is carried out, and a network system is adjusted; installing gateway equipment; the test data is received normally, and the platform can be ensured to receive complete data.
In some preferred modes, the data processing in the monitoring module comprises: when the complete data is acquired, analyzing the completeness and correctness of the data; removing useless data and analyzing the reasons for generation; the monitoring frequency can be increased according to the data feedback condition.
Example 2
A dynamic monitoring method for old and dangerous house collapse based on multivariate sensing data can adopt the system, and comprises the following steps:
(1) carrying out limit load analysis, determining weak point positions, and setting a theoretical early warning threshold;
(2) capturing data of relevant components of the critical house by adopting a sensor;
(3) collecting data captured by a sensor, and sending the data to a server;
(4) reading data stored in a server, and performing threshold value comparison analysis on the data;
(5) and (4) early warning the possible safety accidents and arranging manual inspection.
In specific implementation, the specific process of the step (1) is as shown in fig. 2, original design data of a house are obtained, if the original design data exist, modeling and ultimate load analysis are carried out, weak components are determined, and an early warning threshold value is set; if the original design data of the house does not exist, site reconnaissance is needed, a building plane graph is drawn, a structure construction graph is drawn, material strength detection is carried out, then modeling is carried out, the whole load condition of the building is calculated and analyzed, the collapse limit load of a weak wall body is calculated and analyzed, and an early warning threshold value is set.
As shown in fig. 7, a numerical model is constructed for an old masonry structure. FIGS. 8 and 9 are schematic diagrams of the resistance-to-load effect ratio (φ fA/N) of each layer of wall limb, wherein φ represents the influence coefficient of the high thickness ratio β and the eccentricity e of the axial force on the bearing capacity of the compression member, f represents the designed compressive strength value of the masonry, A represents the cross-sectional area, and N represents the load effect, and the resistance-to-load effect ratio is calculated by PKPM software. In this embodiment, the compressive load capacity of the masonry is calculated by phi fA. The part with a small compression bearing capacity value is selected as a theoretical weak wall limb and is used as a key monitoring object, and a corresponding sensor can be arranged at the part.
Fig. 10 shows a stress cloud chart of the double-opening wall under the action of a vertical load, the stress cloud chart is a stress rainbow chart output by the Abaqus, and in the diagram, from bottom to top, marks with different colors show that the stress gradually increases or the stress concentrates. According to the diagram, under the action of vertical load, the stress concentration position of the wall body appears, and theoretical support is provided for the installation point of the sensing equipment. And (3) placing a deformation sensor such as an inclinometer at a position with larger stress and deformation, such as a position with larger deformation in the beam, and placing a stress meter at the beam end with larger stress.
Fig. 11 is a schematic diagram of the collapse of a double-opening weak wall of a certain masonry structure.
After ABAQUS load is loaded and simulated and analyzed step by step on the wall limb, the limit load which can be born by the wall and the cracking load of the wall are obtained. The material is destroyed by adopting a concrete plastic damage model
Wherein σt、σcRespectively representing tensile stress and compressive stress of the material in a plastic state, E0 representing the initial elastic stiffness of the material, dt and dc respectively representing tensile and compressive damage factors, epsilont、
εc、
Respectively representing tensile strain, tensile equivalent plastic strain, compressive equivalent plastic strain. The numerical calculation provides a theoretical basis for subsequent real-time monitoring. The step-by-step load simulation analysis refers to the following steps: in software simulation, step-by-step load application is set, and when material damage occurs or a wall body cracks, the applied load value at the moment is used as the limit load which can be borne by the structure.
And after theoretical analysis determines monitoring content, arranging sensing equipment for data monitoring.
The static force level gauge monitors the whole and settlement of a house.
In the step (1), the early warning threshold value is set, which can be a threshold value of the integral inclination rate of the house and a threshold value of the settlement amount; such as: according to the actual measurement feedback, the integral inclination rate of the buildings on the second floor and below the second floor is required to be less than 30 per thousand; the integral inclination rate of the house with three or more floors is less than 20 per thousand. And taking the value of the non-rigid masonry structure according to the average value of the single-direction inclination rate. Settling amount: and under the natural state, continuously judging that the number of the months is more than 4 mm/month, and judging that the danger exists if the convergence trend does not exist in a short period. And (3) judging that the construction situation of adjacent underground engineering is dangerous if the two continuous months are more than 4 mm/month, the convergence trend does not exist in a short period, and the sedimentation rate is more than 2 mm/day.
In some preferred modes, in the step (2), a pull rod type displacement meter is used for crack monitoring.
In the step (1), setting an early warning threshold value, and also setting a house crack threshold value; such as monitoring for stressed fractures: the width of the seam of the bearing wall or column is more than 1 mm; a vertical split having a split length exceeding layer height 1/2 or multiple vertical splits exceeding layer height 1/3; a plurality of vertical cracks are generated due to local compression, or the width of the cracks reaches 1 mm; obvious vertical cracks, or obvious oblique cracks at the end part, or stressed cracks generated on the wall body, or obvious bending and downwarping deformation; obvious cracks are generated along the generatrix on the vault of the brick cylinder arch, the flat shell and the wave cylinder arch; the situation that the arch camber or the arch foot generates obvious displacement or the arch body pull rod is seriously corroded and the pull rod system fails is judged as a dangerous situation.
Monitoring of unstressed fractures: vertical cracks of more than 500mm appear; the wall body of the bearing wall body has serious cracks, and the maximum crack width is more than 5 mm. The individual columns exhibit cracks with a width of more than 1.5mm, or evidence of fracture dislocation. Other cracks of significant structural integrity. If one of the above conditions occurs, a danger is determined.
And (2) monitoring the internal force of the bearing wall by adopting a strain gauge.
Phi fA/N can be obtained by analyzing the resistance and load effect of PKPM, and can be obtained by calculating and analyzing general numerical calculation and analysis software such as midas, ABAQUS and the like: and designing the axial force design value of the lower wall limb at the limit state of the bearing capacity. Obtaining a monitoring key component according to the ratio of the resistance to the load effect.
The main structural members (including columns, girders, bearing walls) should satisfy:
general structural members (including secondary beams, lintels, etc.) should satisfy:
wherein, γ0-coefficient of structural importance, Rd-design value of resistance (load-bearing capacity) of the structure; sd-effect design values of load combinations. Gamma ray0·Sd≤RdAnd the formula is a design expression for calculating the bearing capacity limit state.
The strain gauge obtains the stress development change of the strain gauge, and a first, a second and a third early warning levels are designed according to the formula. Primary early warning: the strain count value is more than or equal to 0.85 and less than or equal to 0.9 times of the design value or the calculated value of the bearing capacity; secondary early warning: the value of the strain gauge is more than or equal to 0.9 and less than 1.0 time of the design value or the calculated value of the bearing capacity; and (3) third-level early warning: the value of the strain gauge is more than or equal to 1.0 time of the designed or calculated value of the bearing capacity.
In this embodiment, an inclinometer is used for wall column inclination monitoring.
In the step (1), the early warning threshold value is set, and the threshold value for limiting the local inclined deformation of the whole house can also be set: the single wall or column produces a local inclination deformation greater than 7% relative to the limit of the whole house, representing a structural risk. When the inclined deformation is not more than 7 per thousand, the structure is safe. In the actual monitoring process, the data are acquired by combining an inclinometer, and early warning grade classification is carried out on the actual engineering condition.
A data acquisition and transmission module, and fig. 4 is an overall architecture diagram of a network module. A 12V DC switching power supply is adopted for supplying power, communication is carried out between the LORA network and the sensing node, and data are uploaded through the 4G module; the transmission data adopts a standard Json format, and each packet of data has a time stamp.
In some embodiments, the cloud platform monitoring layer can carry out preliminary early warning according to the numerical value of single sensor, and the manual work can carry out linkage analysis according to the data of other sensors. In other embodiments, like some buildings that the structure is complicated, there is data analysis module on cloud platform monitoring layer, can assist the manual work to carry out the data linkage analysis between the sensor, judges according to the associativity of different sensors during the analysis. For example, the data of the inclinometer and the displacement meter on the same structural column or structural beam are in strong correlation, the method can be used as a detection method for the false alarm and check of a single sensor, and meanwhile, the numerical values and the trends of a plurality of groups of sensors in the same area are compared, so that the alarm confidence level is improved.
Fig. 5 is a flow chart of the operation of the sensing node. Firstly, node slot position address configuration, node function type configuration, communication node hardware address configuration, communication channel configuration and communication baud rate configuration are carried out. The sensing node receives the message, analyzes the message, judges whether the message is effective or not, if the message is effective, data acquisition is carried out, the clock circuit acquires data of the external sensor at regular time, and the message is replied after the acquisition time is up. Specifically, power-on initialization reads sensor node information, including type, RTC clock, LORA information, and unique identifier. Idle running and timing, judging whether the time is overtime for 30s, if not, continuing idle running and timing, and if so, standby and sleeping; whether the mobile phone is awakened or not, if not, the mobile phone is in standby sleep; if yes, analyzing the message, judging whether the message is effective, and if not, sleeping in a standby mode; if yes, starting to collect data timing, and if the timing is not up, returning to continue to collect data timing; if the time is up, the LORA replies the message, waits for the sleep and then circulates. In the invention, the sensing node has the diagnosis functions of electric quantity diagnosis, sensor diagnosis and communication diagnosis.
Fig. 6 is a schematic diagram of a gateway transmission architecture, which is used for configuring a gateway address, configuring functions according to sensor types, configuring an address of node hardware, configuring a communication channel and a communication baud rate, and reading an RTC real-time clock. And collecting sensor message data at regular time and broadcasting and sending the data. And analyzing the data returned by the LORA, reconstructing the Json data packet and then sending the reconstructed Json data packet to the server. And analyzing the server message and responding to the server heartbeat packet. Specifically, power-on initialization is performed, gateway information is read, the gateway information comprises an RTC clock, LORA information and a unique identifier, time slice polling scheduling is performed, whether time is acquired or not is performed, if the time is not acquired, the time slice polling scheduling is performed, if the time is acquired, an LORA awakening acquisition command is sent (for the first time), an LORA awakening acquisition command is sent (for the second time), the LORA awakening acquisition command is sent (for the third time), sensing node data is analyzed, a Json data packet is filled, the data packet is started to be sent, whether the sending is finished or not is performed, if the sending is not performed, the data packet is continuously sent, and if the sending is performed, the next acquisition time is waited, a heartbeat packet is sent every 200s, and the end is performed. The gateway has the diagnosis functions of electric quantity diagnosis, sensor diagnosis and communication diagnosis.
It will be understood by those skilled in the art that all or part of the processes of the above embodiments may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the above embodiments of the methods. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be understood by those skilled in the art that various features of the above-described embodiments can be combined in any combination, and for the sake of brevity, all possible combinations of features in the above-described embodiments are not described in detail, but rather, all combinations of features which are not inconsistent with each other should be construed as being within the scope of the present disclosure.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application.
Claims (10)
1. A dynamic monitoring method for old dangerous house collapse based on multivariate sensing data is characterized by comprising the following steps:
(1) carrying out limit load analysis, determining weak point positions, and setting a theoretical early warning threshold;
(2) capturing data of relevant components of the critical house by adopting a sensor;
(3) collecting data captured by a sensor, and sending the data to a server;
(4) reading and processing data stored in the server, analyzing mechanical characteristics of the data, and performing threshold value comparison analysis;
(5) and (4) early warning the possible safety accidents and arranging manual inspection.
2. The method for dynamically monitoring the collapse of the old dangerous building based on the multivariate sensing data as claimed in claim 1, wherein in the step (2), the adopted sensors are any one or more of a stay-cord type displacement sensor, a pull-rod type displacement sensor, a vibrating wire type strain gauge, a static level gauge and an inclinometer.
3. The dynamic monitoring method for the collapse of the old dangerous building based on the multivariate sensing data as the claim 1, is characterized in that in the step (3), the data captured by the sensor is collected through a Lora network; and packaging the data, converting the data into a 4G network and sending the data to a server.
4. The method for dynamically monitoring the collapse of the old dangerous building based on the multivariate sensing data as set forth in claim 1, wherein the step (5) is specifically as follows: the method has the advantages that the possible safety accidents are early warned, manual patrol is arranged, and old and dangerous rooms which are possible to happen are timely notified to relevant units to carry out crowd evacuation.
5. The method for dynamically monitoring collapse of old dangerous house based on multivariate sensing data as claimed in claim 2, wherein in step (2), a hydrostatic level gauge is adopted to monitor the whole house and the settlement.
6. The dynamic monitoring method for the collapse of the old dangerous building based on the multivariate sensing data as claimed in claim 2, wherein a pull rod type displacement meter is adopted for monitoring cracks.
7. The dynamic monitoring method for the collapse of the old dangerous house based on the multivariate sensing data as recited in claim 2, wherein an inclinometer is used for monitoring the inclination of a wall column.
8. The dynamic monitoring method for the collapse of the old dangerous building based on the multivariate sensing data as claimed in claim 1, wherein in the step (3), the communication is carried out with the sensing node through an LORA network, and the data is uploaded through a 4G module; the transmission data adopts a standard Json format.
9. The method for dynamically monitoring collapse of the old dangerous building based on the multivariate sensing data as claimed in claim 1, is characterized in that a strain gauge is adopted for monitoring the internal force of a bearing wall, and the first-level early warning comprises the following steps: the strain count value is more than or equal to 0.85 and less than or equal to 0.9 times of the design value or the calculated value of the bearing capacity; secondary early warning: the value of the strain gauge is more than or equal to 0.9 and less than 1.0 time of the design value or the calculated value of the bearing capacity; and (3) third-level early warning: the value of the strain gauge is more than or equal to 1.0 time of the designed or calculated value of the bearing capacity; gamma ray0·Sd≤RdThe design expression is calculated for the bearing capacity limit state; gamma ray0-coefficient of structural importance, Rd-design value of resistance of structure; sd-effect design values of load combinations.
10. The method as claimed in claim 7, wherein the single wall or column has a limited local tilt deformation greater than 7% relative to the whole house, representing a structural hazard.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115376292A (en) * | 2022-08-15 | 2022-11-22 | 湖南普奇水环境研究院有限公司 | Real-time monitoring and early warning method and system for house safety |
| CN116045893A (en) * | 2022-12-27 | 2023-05-02 | 中冶建筑研究总院有限公司 | Deformation monitoring system and method for key components of steel structure factory building |
| CN117114404A (en) * | 2023-08-25 | 2023-11-24 | 广东省建设工程质量安全检测总站有限公司 | House safety monitoring method combining manual operation and automatic operation |
| CN115376292B (en) * | 2022-08-15 | 2024-04-16 | 湖南普奇水环境研究院有限公司 | House safety real-time monitoring and early warning method and system |
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